The Rules Of Engagement For Operating Within The Customer Insight Economy

By Larry Mosiman, Worldwide Product Marketing Manager, SAS’ Customer Intelligence Solutions

Almost every article you read today talks about, in one way or another, the down economy. You have to cut costs, layoff staff, renegotiate suppliers contracts – all kinds of dramatic things to survive this down economy. From my perspective, the sense of urgency has been intensified by the economy, but the reality is that this is the same economy as it was one, ten and twenty years ago in many respects. The current economy is a customer insight economy. Whether the economy is a boom or a bust, customers want to be treated fairly and receive value for their money, and companies must have a process in place to ensure that this happens.

In today’s economy, these same customers are holding onto their money tighter, so again, the sense of urgency is higher, but the process is the same. Prerequisite to being successful is to understand your customers by integrating, analyzing, and developing actionable insights from the mounds of customer data that you’ve acquired. In other words, if your customer database wasn’t being used effectively when the economy was good, it’s even more important that it be used effectively now that it’s not.

It starts with the view
Given the state of most enterprise data warehouses, identifying and integrating individual customer data can be difficult. For example, if you are using traditional Web analytics and business intelligence (BI) tools, you probably have lots of information about your business – but little about your prospects and current customers. Your customer support data may be trapped inside the call center. Mission-critical prospect and account data may be trapped in sales. Rarely is individual customer behavior captured and appended to customer profiles to understand channel preferences, communication and information preferences or shopping cart trends. Yet this is the kind of data you need to learn more about each customer, market to them on a one-to-one basis and deliver a differentiated experience across channels.

Contrast this against a scenario where you combine customer and product data from the store, catalog, Web and call center channels along with cross-departmental data from finance, merchandising, investor relations, customer service and operations. Having the data from all of these sources will result in a data warehouse that gives you the ability to see the customer from many different views.

Here’s an example. A large retail company needed a way to centralize its marketing efforts across brands and channels; make communications more relevant to customers; and ultimately increase customer loyalty, purchase frequency and lifetime value. After multiple acquisitions, the company had 14 different brands to manage. Each brand had its own customer and product data scattered across departments managing store, catalog, Web and call center channels. This data fragmentation made it impossible to treat customers as individuals. As a result, marketing efforts were less than effective – the company couldn’t identify its best customers and provide the kind of consistent, differentiated experience needed to retain them.

To turn this around, the company first took care of the basics by integrating all customer data and various external data sources onto a single platform. This allowed them to analyze their customer data, create unique customer insight and use it to increase the relevancy of all communications.

If it’s not actionable, then it isn’t insight
Although companies are looking for every possible way to scale back and cut costs, their customer database (i.e. business intelligence) is not an area that should be on the block. The key to justifying this database is to ensure that they are continually reaping actionable insights from the data. If actionable, the value of the data will go beyond simply understanding their customers to knowing what to do about it. Data should define when and how to communicate with them, knowing what offers to make, knowing when they’re about to leave, and other key indicators. When potential and current customers are under pressure, companies must re-evaluate and re-define what the ‘ideal’ customer is to their organization, because it may have changed. They should look at their most profitable and valuable customers and work to get new ones that look like them, but do it in a timeframe that makes sense. Their best customers today may not be their best customers tomorrow. The bottom line is that no matter how customers are spending their money they can be ranked to determine who among the group are most valuable. But be careful not to have a timeframe that is too big (such as an entire year’s worth of spending) in calculating customer value. Great marketing isn’t just rewarding the customers who love you, it is winning back share from the one’s you’ve ticked off, or the ones that just went into foreclosure and need a hand … and knowing the difference.

Now more than ever …
Since completing the integration work and leveraging the insight gleaned from their customer data, the company discussed above has shown:

• A 30% increase in the direct-mail response rate.


• A 15% increase in orders from e-mail campaigns, due to highly targeted customer communications.


• A 50% increase in repeat purchase rates.


• E-mail, direct mail and outbound phone campaigns targeting customers that produced a
• 70% increase in successful reactivation and a 115% increase in win-back campaigns.


• A 15% increase in the number of “best customers” by targeting programs using customer transaction patterns, as well as a 20% increase in the number of purchase occasions from these customers.

Good and timely BI is essential in allowing marketers to spot the ever changing trends in their customers’ behavior. Knowing when and when not to communicate, what offers to make, what offers not to make, and then executing on this intelligence is what keeps the lights on in the marketing department.

Larry Mosiman has over 20 years of experience leading marketing teams for high tech companies. He is currently the world-wide product marketing manager for SAS’ Customer Intelligence Solutions. Before joining SAS, Mosiman led the marketing organization for the Material Testing Division of MTS Systems Corporation in Eden Prairie, MN, where he developed a successful marketing organization in a business that had been engineering focused.